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--- |
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base_model: JunxiongWang/llama3_mamba_0_5_sft |
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tags: |
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- alignment-handbook |
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- generated_from_trainer |
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datasets: |
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- HuggingFaceH4/ultrafeedback_binarized |
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- HuggingFaceH4/orca_dpo_pairs |
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- JunxiongWang/llama3-ultrafeedback-armorm |
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model-index: |
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- name: JunxiongWang/MambaInLlama_0_50 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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Please check [here](https://github.com/jxiw/MambaInLlama/tree/main) for details. |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/junxiong12/huggingface/runs/zr58yali) |
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# JunxiongWang/MambaInLlama_0_50 |
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This model is a fine-tuned version of [JunxiongWang/llama3_mamba_0_5_sft](https://huggingface.co/JunxiongWang/llama3_mamba_0_5_sft) on the HuggingFaceH4/ultrafeedback_binarized, the HuggingFaceH4/orca_dpo_pairs and the JunxiongWang/llama3-ultrafeedback-armorm datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4002 |
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- Rewards/chosen: -2.2460 |
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- Rewards/rejected: -5.4992 |
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- Rewards/accuracies: 0.8536 |
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- Rewards/margins: 3.2532 |
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- Logps/rejected: -796.0059 |
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- Logps/chosen: -463.1195 |
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- Logits/rejected: -1.1906 |
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- Logits/chosen: -1.2034 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-07 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |
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|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| |
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| 0.4244 | 0.4798 | 2000 | 0.4296 | -2.2555 | -4.8626 | 0.8250 | 2.6071 | -732.3422 | -464.0680 | -1.2867 | -1.2865 | |
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| 0.4311 | 0.9597 | 4000 | 0.4002 | -2.2460 | -5.4992 | 0.8536 | 3.2532 | -796.0059 | -463.1195 | -1.1906 | -1.2034 | |
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### Framework versions |
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- Transformers 4.43.1 |
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- Pytorch 2.1.1+cu118 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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[MambaInLlama](arxiv.org/abs/2408.15237) |
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``` |
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@article{junxiongdaniele2024mambainllama, |
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title = {The Mamba in the Llama: Distilling and Accelerating Hybrid Models}, |
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author = {Junxiong Wang and Daniele Paliotta and Avner May and Alexander M. Rush and Tri Dao}, |
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journal = {arXiv preprint arXiv:2408.15237}, |
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year = {2024} |
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} |
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``` |
